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Record W2116017095 · doi:10.1016/j.pain.2011.01.028

Norming of the Tampa Scale for Kinesiophobia across pain diagnoses and various countries

2011· article· en· W2116017095 on OpenAlexaffabout
Jeffrey Roelofs, Gerard van Breukelen, Judith Sluiter, Monique H. W. Frings‐Dresen, Mariëlle E. J. B. Goossens, Pascal Thibault, Katja Boersma, Johan W.S. Vlaeyen

Bibliographic record

VenuePain · 2011
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcGill University
FundersVlaamse regeringNederlandse Organisatie voor Wetenschappelijk OnderzoekFonds Wetenschappelijk Onderzoek
KeywordsFibromyalgiaPsychologyPhysical therapyMedical diagnosisClinical psychologyPsychiatryMedicinePathology

Abstract

fetched live from OpenAlex

The present study aimed to develop norms for the Tampa Scale for Kinesiophobia (TSK), a frequently used measure of fear of movement/(re)injury. Norms were assessed for the TSK total score as well as for scores on the previously proposed TSK activity avoidance and TSK somatic focus scales. Data from Dutch, Canadian, and Swedish pain samples were used (N=3082). Norms were established using multiple regression to obtain more valid and reliable norms than can be obtained by subgroup analyses based on age or gender. In the Dutch samples (N=2236), pain diagnosis was predictive of all TSK scales. More specifically, chronic low back pain displayed the highest scores on the TSK scores followed by upper extremity disorder, fibromyalgia, and osteoarthritis. Gender was predictive of TSK somatic focus scores and age of TSK activity avoidance scores, with male patients having somewhat higher scores than female patients and older patients having higher scores compared with younger patients. In the Canadian (N=510) and Swedish (N=336) samples, gender was predictive of all TSK scales, with male patients having somewhat higher scores than female patients. These norm data may assist the clinician and researcher in the process of decision making and treatment evaluation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.266
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations131
Published2011
Admission routes2
Has abstractyes

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